Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
whisper
Generated from Trainer
Eval Results (legacy)
Instructions to use sanchit-gandhi/whisper-large-v3-ft-cv16-mn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sanchit-gandhi/whisper-large-v3-ft-cv16-mn with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="sanchit-gandhi/whisper-large-v3-ft-cv16-mn")# Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("sanchit-gandhi/whisper-large-v3-ft-cv16-mn") model = AutoModelForSpeechSeq2Seq.from_pretrained("sanchit-gandhi/whisper-large-v3-ft-cv16-mn", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| python run_speech_recognition_seq2seq.py \ | |
| --model_name_or_path="openai/whisper-large-v3" \ | |
| --dataset_name="mozilla-foundation/common_voice_16_0" \ | |
| --dataset_config_name="mn" \ | |
| --language="mongolian" \ | |
| --train_split_name="train+validation" \ | |
| --eval_split_name="test" \ | |
| --num_train_epochs="10" \ | |
| --output_dir="./" \ | |
| --per_device_train_batch_size="32" \ | |
| --per_device_eval_batch_size="32" \ | |
| --logging_steps="25" \ | |
| --learning_rate="1e-4" \ | |
| --warmup_steps="500" \ | |
| --evaluation_strategy="steps" \ | |
| --eval_steps="500" \ | |
| --save_strategy="steps" \ | |
| --save_steps="1000" \ | |
| --generation_max_length="225" \ | |
| --length_column_name="input_length" \ | |
| --max_duration_in_seconds="30" \ | |
| --text_column_name="sentence" \ | |
| --freeze_feature_encoder="False" \ | |
| --streaming="False" \ | |
| --gradient_checkpointing \ | |
| --fp16 \ | |
| --fp16_full_eval \ | |
| --overwrite_output_dir \ | |
| --do_train \ | |
| --do_eval \ | |
| --predict_with_generate \ | |
| --use_auth_token \ | |
| --push_to_hub | |